Database systems have traditionally allowed for functionality to store, retrieve, and manage vast amounts of information. Increasingly, large business organizations utilize a variety of database systems across multiple business units. Although there are numerous data storage transactional systems, the primary classification of these systems from a business standpoint is along operational or warehousing lines, with Online Transaction Processing (OLTP) systems oriented towards current operation of a business, and a data warehouse geared towards providing longer term, management oriented questions about the business. Large amounts of normalized data are moved from the OLTP system, de-normalized reordered, aggregated, transformed and reloaded into a data warehouse in a periodic manner so as to stay relatively current with the OLTP system.
These OLTP databases typically represent the current state of any operational system. Information is entered as data records/tuples via transactions that move the database from one consistent state to another; these systems are adept at keeping track of items (data) and relationships (dependencies, constraints) as they change, and facilitating new transactions. OLTP databases track detailed information for the purposes of current operations. OLTP databases do not generally maintain comprehensive historical information (typically only a few months worth). for reasons of speed and economy.
OLTP databases are not optimized to facilitate the following types of activities:                Comparing data and related activities between different periods        Running point-in-time queries        Tracing specific activity during a given time period        Showing evolution of data records        Identifying and quickly reacting to interesting transactions        Browsing any database table and row activity within a specific time period        Linking specific row changes to related transactions        Reapplying transactions selectively        
Data Warehouses are not optimized to facilitate the following tasks:                Providing all historical operational changes. For e.g. the most common approach to move data into a data warehouse is using ETL utilities. Such utilities may not capture all historical changes between two successive ETL passes since not all data source systems may have an indicator flag, or reliable timestamp of when a new change occurred.        Providing all records that constituted one transaction at a data source.        Providing, retaining transactional context such as all related operations that were part of the transaction at the exact commit point when the change(s) were made.        Providing a trace lineage to the data source origin especially with respect to) ,he posting of the original transaction at the data source.        Querying over data that has not been loaded into the warehouse.        Providing a non intrusive uniform automatic change data capture method to obtain data from original data sources.        
Considering the increasing need for businesses to help address these tasks and challenges, it is desirable to provide a new type of data store that facilitates operations that are difficult or not possible to support with current OLTP databases and data warehouses.